Visible–near-infrared–shortwave-infrared (VNIR–SWIR) spectroscopy is one of the most promising sensing techniques to meet ever-growing demands for soil information and data. To ensure the successful application of this technique in the field, efficient methods for tackling detrimental moisture effects on soil spectra are critical. In this paper, mathematical techniques for reducing or removing the effects of soil moisture content (SMC) from spectra are reviewed. The reviewed techniques encompass the most common spectral pre-processing and algorithms, as well as less frequently reported methods including approaches within the remote sensing domain. Examples of studies describing their effectiveness in the search for calibration model improvement are provided. Moreover, the advantages and disadvantages of the different techniques are summarized. Future research including further studies on a wider range of soil types, in-field conditions, and systematic experiments considering several SMC levels to enable the definition of threshold values for the effectiveness of the discussed methods is recommended.

A WebGis tool called GoProsit has been developed to support winegrowers in planning a new sustainable vineyard and in the identification of high-quality terroir in Tuscany, Central Italy, by providing various information on soils, climate, hydrological risks, and fertilization. GoProsit, hosted by the web platform GEAPP, is a free, user-friendly, and interactive Geographic Decision Support System (GDSS). Soil data behind the WebGis tool has a 1 ha resolution, achieved by processing the legacy vector-type soil database of the Tuscany Region with the DSMART (Disaggregation and Harmonization of Soil Map Units Through Resampled Classification Trees as supervised classification) algorithm, which disaggregated the map to 297,023 vineyard grid cells. Each grid cell holds climatic and pedologic information, along with physical and chemical features for each horizon of the most probable soil. GoProsit also provides soil maps in image format obtained by georeferencing about 50 historical soil maps (1969–2012). Finally, GoProsit runs and returns the outputs of six models: (a) carbon footprint, (b) potential erosion and maximum vine row length compatible with tolerable erosion, (c) potential water stress, (d) risk of runoff/waterlogging, (e) identification of suitable rootstocks, and (f) nutritional needs before planting. Statistics of the main model results for the investigated area are reported. This promising tool will soon be usable for the whole Italian territory; however, its potential makes it suitable for use in any wine-growing district.

Determining soil organic carbon (SOC) stock and its changes over time is crucial for understanding carbon cycling. This study evaluates the reliability of visible and near-infrared (Vis-NIR) spectroscopy as a cost-effective method for detecting SOC within monitoring, reporting, and verification (MRV) systems. Soil samples from a long-term field experiment (LTE) in northern Italy, comparing maize-based forage systems were used as a case study. Three sampling campaigns (2003, 2012, and 2018) were utilized for a total of 162 soil samples collected in the LTE (54 each). Soil samples archived were retrieved and scanned using a Vis-NIR spectrometer to create a site-specific soil spectral library (Site-SSL). Aiming to implement a local prediction model samples collected in 2003 were used as a training dataset to estimate the SOC of the soil samples collected in 2012 and 2018. Concurrently, a second prediction model was run adding 172 regional soil samples (Reg-SSL) collected the same soil-landscape as the LTE. N.4 model strategies were compared, including random forest (RF), cubist (CU), memory based learning (MBL) and support vector machine (SVM) on Site-SSL and Reg-SSL. A sensitivity analysis was performed to evaluate the impact of training sample size, followed by an assessment of the cost-benefit of spectroscopic approach compared to conventional analysis. The results showed that the Vis-NIR spectral libraries, along with the CU and SVM models, were able to detect changes in SOC in the Site-SSL dataset, yielding the best results. To maintain optimal performance, it is advisable to include the standard analyses of at least 10 percent of the subsequent monitoring samples in the training set.

We provide an overview of the accuracy of soil property predictions using the most common proximal sensing (PSS) techniques in precision agriculture (PA), both standalone and in combination with one another or with environmental covariates. Based on 114 scientific papers, we evaluate the accuracy of soil property estimates by calculating the normalized root mean square error (NRMSE) using RMSE values and the range of the predicted soil property. Soil properties, PSS techniques, covariate types, and the model employed for predictions are the factors used to sort the accuracy results. We estimate PSS service costs using both the literature and a market study with questionnaires from private companies in PA. Our analysis indicates that diffuse reflectance spectroscopy (DRS) can estimate the greatest number of soil properties with high accuracy among the PSS techniques. Popular DRS applications include determining soil organic matter, nutrients, and soil texture. X-ray fluorescence (XRF) is the second-most popular technique for estimating soil properties. XRF is widely used in the field to determine elemental concentrations. On-the-go techniques such as electromagnetic induction (EMI) or gamma-ray spectroscopy (γ-ray) yield lower accuracy than point-based techniques. They are widely used by companies because they can delineate PA management zones in the field and are suitable for on-the-go mapping of soil properties such as mineralogy, texture, salinity, water content, cation exchange capacity, and soil depth. The combined use of PSS techniques generally doesn’t outperform the singular application, although the number of samples collected for calibration and the specific combinations of sensors, covariates, and modeling techniques, when correctly applied, may enhance the predictions of soil properties using PSS techniques applied singularly. These outcomes tend to depend on local site characteristics.According to data, the estimated cost of surveying a hectare with PSS oscillates between 15.5€/ha and 130€/ha, whereas our company survey yielded an interval of 142-362€/ha. Price variability was influenced by personnel costs, fieldwork, data and reporting, sample analysis, and equipment. Increases in final prices can be attributed to accessibility and difficulties related to fieldwork and travel to the area of interest. This work aims to serve as a reference for the adoption of sensing technologies by farmers, policymakers, and companies, providing insights into the suitability of different PSS techniques for soil mapping, their associated costs, and what is available in the market. We foresee that PSS will become the standard approach for producing high-resolution maps and affordable soil property information in the future. AcknowledgmentsWork funded by the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement Nº 862695, and from the Tillämpninggsklivet Precisionsodling RUN 2021-00020 Region Västra Götaland

The Council Agricultural Research and Economics, Research Centre for Agriculture and Environments of Florence, (CREA-AA), National Soil Archive (ARCAN) is an important resource for current and future soil research. ARCAN holds 32,612 archived specimens, collected from 13,156 sites across Italy. The ARCAN is also very relevant from a historical and agroecological point of view, collecting soil samples from surface and subsurface horizons, in some cases taken before Chernobyl and almost all after Chernobyl (1986-2021). In particular, 7,000 agricultural samples are representative of all regions of Italy, collected in the same year (1994), with the same standard sampling and analysis protocol. In this work we show up a new database management system (DATA-RAS) to achieve Vis-Nir spectroscopy data and environment radioactivity data on ARCAN soil samples. Combined use of Vis-Nir and gamma ray spectroscopy to predict soil properties offers a task to populate the database. After that it is possible to obtain a digital soil map of artificial or natural radioactivity of Italian soils. The main purpose of this work is show up an Entity Relationship model of DATA-RAS, that represents other than a database a Smart Data Exchange a system to better managing ARCAN and rapidly exchanging information with other national and European partners. The ultimate goal is to use the most advanced information and communication technologies to create an innovative platform that digitally manages open data with 3D methods and augmented and virtual reality techniques (AR and VR). The DATA-RAS platform will be integrated with scientific information systems and a natural language interface for knowledge communication.